In this paper we extend Badzioch's, Dorabiala's, and Williams' definition of cohomological higher smooth torsion to a twisted cohomological higher torsion invariant. Additionally, we show that this still satisfies geometric additivity and transfer, and will also satisfy additivity and transfer for coefficients.
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48 results for “additive transfer”
New algorithm identifies best target arm with known additive relationship between source and target MAB instances.
problem Identifying the best arm in a target MAB instance when only source arms can be pulled and there's a known additive relationship between the two.
method Proposes an LUCB-style algorithm to identify an ε-optimal target arm with high probability.
result Theoretical analysis highlights aspects of the transfer learning problem and recovers the LUCB algorithm for single domain BAI as a special case.
Zero-shot contrastive loss improves text-guided image style transfer without extra training.
problem Stochastic nature of diffusion models leads to trade-offs between style transformation and content preservation.
method Proposes a zero-shot contrastive loss for diffusion models that doesn't require additional fine-tuning or auxiliary networks.
result Method outperforms existing methods while preserving content and requiring no additional training.
Additive models form a widely popular class of regression models which represent the relation between covariates and response variables as the sum of low-dimensional transfer functions. Besides flexibility and accuracy, a key benefit of these models is their interpretability: the transfer functions provide visual means…
Proposes a new model to predict polymer properties by integrating various data types.
problem Inaccurate polymer property prediction due to separate modeling of different data types.
method Multi-modal cascade feature transfer using GCN for chemical structure and molecular descriptors.
result Empirically evaluated model shows higher predictive performance than single-feature approaches.
Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function a neuron should perform. This leads either to an inefficient distribution of computational resources or an extensive increase in the comput…
New methods improve electricity load forecasting using hierarchical transfer learning.
problem Improving electricity load forecasts at national scale using smart meter data.
method Developed two hierarchical transfer learning methods based on stacking and aggregation of experts.
result Significant improvement in predictions compared to benchmark algorithms.
Adaptive source selection for positive transfer in linear models improves target dataset performance.
problem Limited task-specific labeled data in business settings.
method Greedily decides from which sources and how many samples to incorporate into the target dataset using an accept/reject rule based on a data-dependent estimate of the transfer gain.
result Consistent gains over classical and recent strong baselines while avoiding negative transfer.
The paper develops a minimax optimal method for high-dimensional regression using auxiliary data.
problem High-dimensional additive regression with heavy-tailed errors and transfer learning.
method Smooth backfitting estimator with local linear smoothing, followed by a two-stage estimation method.
result The method achieves the minimax optimal rate under certain conditions.
Study examines the impact of target data in transfer learning.
problem Understanding the value of target data in transfer learning.
method Established minimax-rates for source and target sample sizes, introduced transfer exponents.
result Performance limits in transfer learning are captured by transfer exponents.
A method for clustering using transfer learning from similar labeled data.
problem Clustering with datasets having different features and labeled data.
method Constructing meta-features to describe structural characteristics of data and transferring them between source and target domains.
result The method is efficient and works under arbitrary feature descriptions of source and target domains with smaller complexity.
Deep generative models such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs) have recently been applied to style and domain transfer for images, and in the case of VAEs, music. GAN-based models employing several generators and some form of cycle consistency loss have been among the most suc…
DDSTN improves breast cancer diagnosis by leveraging imbalanced ultrasound modalities.
problem Imbalanced ultrasound modalities in diagnosing breast cancer.
method Integrates LUPI and MMD into a deep transfer learning framework.
result Outperforms state-of-the-art algorithms in BUS-based CAD.
Research has shown that widely used deep neural networks are vulnerable to carefully crafted adversarial perturbations. Moreover, these adversarial perturbations often transfer across models. We hypothesize that adversarial weakness is composed of three sources of bias: architecture, dataset, and random initialization.…
Adversarial perturbations fool wearable sensor systems, showing transferability across different systems.
problem Adversarial examples fool wearable sensor systems, showing transferability across different systems.
method Study of adversarial transferability in wearable sensor systems from four perspectives: systems, subjects, sensor body locations, and datasets.
result Strong untargeted transferability in most cases, targeted attacks less successful.
In this work, we evaluate adversarial robustness in the context of transfer learning from a source trained on CIFAR 100 to a target network trained on CIFAR 10. Specifically, we study the effects of using robust optimisation in the source and target networks. This allows us to identify transfer learning strategies unde…
New findings on representation changes in transfer learning.
problem Understanding representation changes in transfer learning.
method Analysis of pre-trained and fine-tuned representations in transfer learning.
result Representation changes mainly occur in the head, but early layers are also important for cross-domain adaptation.
LGV boosts adversarial attacks by improving surrogate models.
problem Improving the transferability of black-box adversarial attacks.
method LGV uses a pretrained surrogate model and multiple weight sets from additional training epochs to generate an effective surrogate ensemble.
result LGV outperforms other test-time transformations by significant margins.
Paper tackles continuous transfer learning with evolving target domains.
problem Challenges of negative transfer in evolving target domains.
method Proposes label-informed C-divergence for measuring distribution shift and negative transfer.
result Demonstrates effectiveness of TransLATE framework in minimizing classification error and C-divergence.
Transfer learning and data augmentation improve stock classification performance.
problem Challenges in stock classification due to noise and volatility.
method Pre-trained model on S&P500 index features, transfer learning to new models, data augmentation on feature space.
result Augmentation on feature space leads to 20% increase in risk-adjusted returns.
Develops a method for manifold learning with small sample size datasets.
problem Improving manifold learning performance for multiple tasks with limited samples.
method Uses instance and model transfer to integrate manifold models from similar tasks.
result Successfully estimates manifolds with tiny sample sizes across multiple tasks.
Double Double Descent: On Generalization Errors in Transfer Learning between Linear Regression Taskscs.LG
Double descent in transfer learning explained for linear regression problems.
problem Understanding generalization errors in transferring parameters between overparameterized linear regression tasks.
method Analytical characterization of generalization error in terms of transfer learning factors.
result Generalization error follows a two-dimensional double descent trend controlled by transfer learning factors.
Provable Sample-Efficient Transfer Learning Conditional Diffusion Models via Representation Learningcs.LG
Paper improves sample efficiency of transfer learning in diffusion models.
problem Diffusion models need too much data to train from scratch.
method Assumes shared low-dimensional representation across tasks for improved sample efficiency.
result Sample complexity of target tasks can be reduced with a well-learned representation.
Bitcoin's price direction is better predicted without additional drivers during high volatility.
problem Predicting Bitcoin's price direction using various determinants.
method Continuous local transfer entropy for feature selection and deep learning classification model.
result Bitcoin's price direction can be better predicted without additional drivers during high volatility.
New study reveals surprising adaptive rates in model selection for transfer learning.
problem Model selection in transfer learning with varying transfer distances.
method Theoretical analysis focusing on classification.
result Adaptive rates can be arbitrarily slower than oracle rates.
Framework for transferring discount curve estimates across fixed-income product classes.
problem Challenges in estimating discount curves from sparse or noisy data.
method Proposes a vector-valued kernel ridge regression (KR) framework with economic regularization.
result Transfer learning tightens confidence intervals and improves extrapolation performance.
Improved language models with talking-heads attention.
problem Language model perplexity and quality issues.
method Added linear projections in multi-head attention.
result Better perplexities and quality in language tasks.
Behavior Transfer improves reinforcement learning by leveraging pre-trained policies.
problem Efficient transfer of knowledge in reinforcement learning.
method Behavior Transfer (BT) that uses pre-trained policies for exploration.
result BT combined with pre-training leads to better solutions than without pre-training.
Enhances VAR model estimation using transfer learning.
problem Estimating high-dimensional VAR models with temporal dependencies.
method Transfer learning for VAR models with low-rank and sparse structures.
result Theoretical guarantees for model parameter consistency and informative set selection.
We propose a framework that learns a representation transferable across different domains and tasks in a label efficient manner. Our approach battles domain shift with a domain adversarial loss, and generalizes the embedding to novel task using a metric learning-based approach. Our model is simultaneously optimized on …
This paper explores how the generalization of substitute classifiers affects the success of black-box adversarial attacks.
problem Understanding the factors driving the transferability of black-box adversarial examples.
method Max-min adversarial example game framework and theoretical generalization bounds.
result Substitute NN with better generalization behavior results in more transferable adversarial examples.
SOR framework improves transfer learning efficiency and adaptability.
problem Limited data and overfitting in transfer learning.
method Freezes network structures while using group lasso and penalties.
result Achieves competitive results on few-shot medical imaging tasks.
Paper classifies Parkinson's disease from speech in three languages using CNNs and transfer learning.
problem Classifying Parkinson's disease from speech in multiple languages.
method Convolutional Neural Networks (CNNs) with transfer learning among Spanish, German, and Czech.
result Transfer learning improves model accuracy by up to 8% and balances specificity-sensitivity.
Transfer learning improves HVAC fault detection with minimal labeled data.
problem Lack of labeled data for HVAC system faults.
method Bayesian classifier trained on large data, then transferred to new buildings with few normal operation samples.
result Few samples are sufficient to maintain classifier precision and recall.
Proposes a transfer learning method for PCA studies.
problem Enhancing PCA estimation accuracy across multiple studies.
method Two-step algorithm integrating shared subspace information via Grassmannian barycenter.
result Knowledge transfer improves PCA estimation accuracy through enlarged eigenvalue gap.
One of the defining characteristics of human creativity is the ability to make conceptual leaps, creating something surprising from typical knowledge. In comparison, deep neural networks often struggle to handle cases outside of their training data, which is especially problematic for problems with limited training dat…
Paper presents Transfer Portal model for accurate player performance predictions.
problem Predicting future player performance after a transfer.
method Personalized neural network and Bayesian updating framework.
result Model generates accurate predictions for player performance at new clubs.
The paper addresses classification imbalance by framing it as a transfer learning problem.
problem Classification imbalance where one class is much rarer than the other.
method The paper studies oversampling procedures to balance classes, focusing on SMOTE and bootstrapping.
result The excess risk decomposes into balanced training rate and transfer cost, with SMOTE having a higher transfer cost.
Existing approaches to combine both additive and multiplicative neural units either use a fixed assignment of operations or require discrete optimization to determine what function a neuron should perform. However, this leads to an extensive increase in the computational complexity of the training procedure. We present…
This paper examines data transfer methods to improve off-the-shelf Transformer models for retrosynthesis.
problem Improving prediction models for retrosynthesis using small datasets.
method Systematic examination of data transfer approaches on end-to-end generative models.
result Pre-training plus fine-tuning boosts the accuracy of a Transformer baseline, achieving state-of-the-art results.
Adversarial training enhances model transferability without sacrificing accuracy.
problem The principle of minimal information in classification models is challenged by adversarial training.
method Investigation of the dual relationship between adversarial training and information theory.
result Adversarial training improves linear transferability and introduces a trade-off between transferability and source task accuracy.
New method creates powerful, transferable adversarial attacks for deep learning networks.
problem Vulnerability of deep learning networks to adversarial attacks.
method Developed a method to generate strong, transferable adversarial attacks.
result Demonstrated that the proposed method can easily transfer strong adversarial attacks between different models.
Recent works reveal that network embedding techniques enable many machine learning models to handle diverse downstream tasks on graph structured data. However, as previous methods usually focus on learning embeddings for a single network, they can not learn representations transferable on multiple networks. Hence, it i…
Adapts Neyman-Pearson classification for both source and target distribution shifts.
problem Minimizing errors while controlling both Type-I and Type-II errors under distribution shifts.
method Derives an adaptive procedure that guarantees improved error rates and adapts to uninformative sources.
result Automatic adaptation to uninformative sources avoids negative transfer.
Paper tackles transfer learning for contextual multi-armed bandits under covariate shift.
problem Nonparametric contextual multi-armed bandits with covariate shift.
method Established minimax rate of convergence, proposed transfer learning algorithm.
result Achieved near-optimal statistical guarantees for learning in target domain.
When related learning tasks are naturally arranged in a hierarchy, an appealing approach for coping with scarcity of instances is that of transfer learning using a hierarchical Bayes framework. As fully Bayesian computations can be difficult and computationally demanding, it is often desirable to use posterior point es…
DeepInversion generates images from trained networks without additional data.
problem Generating images from trained networks without real data.
method Inverting a trained network to synthesize images from random noise, optimizing using batch normalization layers and maximizing Jensen-Shannon divergence.
result Synthesized images from CIFAR-10 and ImageNet datasets show high fidelity and realism.
Personalized stress model using transfer learning from 20 participants.
problem Limited generalizability of machine learning models due to individual physiological differences.
method Transfer learning from a base model trained on 20 participants' physiological data collected in real-time.
result Improved model personalization and cross-domain performance.